Excessive emissions of nitrogen oxides from flue gas have imposed various detrimental impacts on environment,and the development of deNO_(x) catalysts with low-cost and high performance is an urgent requirement.Iron o...Excessive emissions of nitrogen oxides from flue gas have imposed various detrimental impacts on environment,and the development of deNO_(x) catalysts with low-cost and high performance is an urgent requirement.Iron oxide-based material has been explored for promising deNO_(x) catalysts.However,the unsatisfactory low-temperature activity limits their practical applications.In this study,a series of excellent low-temperature denitrification catalysts(Ha-FeO_(x)/yZS)were prepared by acid treatment of zinc slag,and the mass ratios of Fe to impure ions was regulated by adjusting the acid concentrations.Ha-FeO_(x)/yZS showed high denitrification performance(>90%)in the range of 180–300℃,and the optimal NO conversion and N2 selectivity were higher than 95%at 250℃.Among them,the Ha-FeO_(x)/2ZS synthesized with 2 mol/L HNO3 exhibited the widest temperature window(175–350℃).The excellent denitrification performance of Ha-FeO_(x)/yZS was mainly attributed to the strong interaction between Fe and impurity ions to inhibit the growth of crystals,making Ha-FeO_(x)/yZS with amorphous structure,nice fine particles,large specific surface area,more surface acid sites and high chemisorbed oxygen.The in-situ DRIFT experiments confirmed that the SCR reaction on the Ha-FeO_(x)/yZS followed both Langmuir-Hinshelwood(L-H)mechanism and Eley-Rideal(E-R)mechanism.The present work proposed a high value-added method for the preparation of cost-effective catalysts from zinc slag,which showed a promising application prospect in NO_(x) removal by selective catalytic reduction with ammonia.展开更多
The radiant tube burner was modeled and analyzed by the numerical simulation method to investigate the influence factors and rules of NO_(x) emissions in a W-type radiant tube.These factors,which include air preheatin...The radiant tube burner was modeled and analyzed by the numerical simulation method to investigate the influence factors and rules of NO_(x) emissions in a W-type radiant tube.These factors,which include air preheating temperature,excess air coefficient,and fuel gas composition,were modified to study their effects on NO_(x) emissions under varying working conditions.Simulation results were compared with the theoretical calculation value based on chemical reaction equilibrium theory and the onsite experimental value to verify the simulation accuracy.The results show that NO_(x) emissions rise with increasing air preheating temperatures.NO_(x) production increases to an extreme value and then decreases during the oxygen-poor to oxygen-enriched process with the rise of the excess air coefficient.Enhancing the proportion of coke oven gas in the fuel gas raises the combustion temperature as well as the NO_(x) discharge.Both the thermal efficiency and NO_(x) emissions should be balanced.Therefore,the recommended values based on the simulation results are as follows:the air preheating temperature should not exceed 400℃,the excess air coefficient should be between 1.1 and 1.2,and the volume fraction of the coke oven gas should not exceed 30%.展开更多
锅炉燃烧优化在电厂锅炉经济稳定运行中起着重要作用,NO_(x)排放预测是其中的一个基本环节,因此提出了一种基于改进蜣螂优化算法优化卷积神经网络(convolutional neural network,CNN)与双向长短期记忆神经网络(long short term memory,L...锅炉燃烧优化在电厂锅炉经济稳定运行中起着重要作用,NO_(x)排放预测是其中的一个基本环节,因此提出了一种基于改进蜣螂优化算法优化卷积神经网络(convolutional neural network,CNN)与双向长短期记忆神经网络(long short term memory,LSTM)的组合模型超参数的超超临界锅炉NO_(x)排放预测的方法。首先通过Pearson相关性判定与NO_(x)排放相关的特征参数;其次建立CNN-LSTM预测模型,利用卷积神经网络CNN提取分层数据结构,长短期记忆网络挖掘长期依赖关系,然后结合佳点集、t分布变异策略对蜣螂算法进行改进,用改进后的算法对LSTM超参数进行优化得到最终预测模型;最后与其他神经网络模型进行对比验证。以某660 MW机组锅炉深度调峰实际数据进行预测,结果得到NO_(x)排放浓度实际值与预测值的平均绝对误差为3.3516,平均相对误差为2.4667,数据结果表明该预测模型具有更准确的预测效果。展开更多
基金National Natural Science Foundation of China(21676209)Natural Science Basic Research Program of Shaanxi(2022JQ-328)Postdoctoral Research Foundation of the Xi’an University of Architecture and Technology(19603210120).
文摘Excessive emissions of nitrogen oxides from flue gas have imposed various detrimental impacts on environment,and the development of deNO_(x) catalysts with low-cost and high performance is an urgent requirement.Iron oxide-based material has been explored for promising deNO_(x) catalysts.However,the unsatisfactory low-temperature activity limits their practical applications.In this study,a series of excellent low-temperature denitrification catalysts(Ha-FeO_(x)/yZS)were prepared by acid treatment of zinc slag,and the mass ratios of Fe to impure ions was regulated by adjusting the acid concentrations.Ha-FeO_(x)/yZS showed high denitrification performance(>90%)in the range of 180–300℃,and the optimal NO conversion and N2 selectivity were higher than 95%at 250℃.Among them,the Ha-FeO_(x)/2ZS synthesized with 2 mol/L HNO3 exhibited the widest temperature window(175–350℃).The excellent denitrification performance of Ha-FeO_(x)/yZS was mainly attributed to the strong interaction between Fe and impurity ions to inhibit the growth of crystals,making Ha-FeO_(x)/yZS with amorphous structure,nice fine particles,large specific surface area,more surface acid sites and high chemisorbed oxygen.The in-situ DRIFT experiments confirmed that the SCR reaction on the Ha-FeO_(x)/yZS followed both Langmuir-Hinshelwood(L-H)mechanism and Eley-Rideal(E-R)mechanism.The present work proposed a high value-added method for the preparation of cost-effective catalysts from zinc slag,which showed a promising application prospect in NO_(x) removal by selective catalytic reduction with ammonia.
文摘The radiant tube burner was modeled and analyzed by the numerical simulation method to investigate the influence factors and rules of NO_(x) emissions in a W-type radiant tube.These factors,which include air preheating temperature,excess air coefficient,and fuel gas composition,were modified to study their effects on NO_(x) emissions under varying working conditions.Simulation results were compared with the theoretical calculation value based on chemical reaction equilibrium theory and the onsite experimental value to verify the simulation accuracy.The results show that NO_(x) emissions rise with increasing air preheating temperatures.NO_(x) production increases to an extreme value and then decreases during the oxygen-poor to oxygen-enriched process with the rise of the excess air coefficient.Enhancing the proportion of coke oven gas in the fuel gas raises the combustion temperature as well as the NO_(x) discharge.Both the thermal efficiency and NO_(x) emissions should be balanced.Therefore,the recommended values based on the simulation results are as follows:the air preheating temperature should not exceed 400℃,the excess air coefficient should be between 1.1 and 1.2,and the volume fraction of the coke oven gas should not exceed 30%.
文摘锅炉燃烧优化在电厂锅炉经济稳定运行中起着重要作用,NO_(x)排放预测是其中的一个基本环节,因此提出了一种基于改进蜣螂优化算法优化卷积神经网络(convolutional neural network,CNN)与双向长短期记忆神经网络(long short term memory,LSTM)的组合模型超参数的超超临界锅炉NO_(x)排放预测的方法。首先通过Pearson相关性判定与NO_(x)排放相关的特征参数;其次建立CNN-LSTM预测模型,利用卷积神经网络CNN提取分层数据结构,长短期记忆网络挖掘长期依赖关系,然后结合佳点集、t分布变异策略对蜣螂算法进行改进,用改进后的算法对LSTM超参数进行优化得到最终预测模型;最后与其他神经网络模型进行对比验证。以某660 MW机组锅炉深度调峰实际数据进行预测,结果得到NO_(x)排放浓度实际值与预测值的平均绝对误差为3.3516,平均相对误差为2.4667,数据结果表明该预测模型具有更准确的预测效果。
